With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors or only with those pre-selected by a hydrologist. The models used in this study were multivariate linear regression, the M5 model tree, multilayer perceptron (MLP) artificial neural network, and the long short-term memory (LSTM) model. We used two river catchments in contrasting runoff generation conditions to try to infer the ability of different model structures to automatically select the best predictor set from all those available in the dataset and compared models’ performance with that of a model operating on predictors prescribed by a hydrologist. Additionally, we tested how shuffling of the initial dataset improved model performance. We can conclude that in rainfall-driven catchments, the models performed generally better on a dataset prescribed by a hydrologist, while in mixed-snowmelt and baseflow-driven catchments, the automatic selection of predictors was preferable.

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.

A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April–June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

In this study, the downstream effects of pollutants spreading due to hydromorphological gradients and associated changes in sediment transport conditions along the braided-meandering and deltaic distributary reach of a large river downstream section are discussed. We demonstrate the significance of hydrodynamic control for sediment-associated metal partitioning along the river. Typically, the downward decline of the sediment and metals spreading towards Lake Baikal is observed due to buffer effects in the delta. During peak flow, the longitudinal gradients in heavy metal concentration along the distributary delta reach are neglected due to higher concentrations delivered from the upper parts of the river. In particular, significant variations of heavy metal concentrations associated with the river depth are related to sediment concentration and flow velocity profiles. Various particulate metal behavior in silt-sand delta channels and the sand–gravel Selenga main stem emphasize the importance of near-bottom exchange for particles spreading with the river flow. Using empirically derived Rouse numbers, we found quantitative relationships between the ratio of particulate metals sorting throughout depth in a single river channel and the hydrodynamic conditions of sediment transport.

This paper considers the main principles and technologies used in developing the operational modeling system for the Ussuri River Basin of 24,400 km² based on the automated system of hydrological monitoring and data management (ASHM), the physical-mathematical model with distributed parameters ECOMAG (ECOlogical Model for Applied Geophysics) and the numerical mesoscale atmosphere model WRF (Weather Research and Forecasting Model). The system is designed as a freely combined tool that allows flexible changing of the forecasting and informational components. The technology of inter-model and cross-platform interoperability is based on the use of the Simple Object Access Protocol (SOAP) web services and the Open Geospatial Consortium Open Modelling Interface (OGC OpenMI) standard. The system demonstrates good performance in short-term forecast of rainfall floods and reproduces complex spatio-temporal structure for the runoff formation during extreme rainfall.